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14 pages, 2222 KiB

Open AccessArticle

The Prediction of LptA and LptC Protein–Protein Interactions and Virtual Screening for Potential Inhibitors

by Yixin Ren, Wenting Dong, Yan Li, Weiting Cao, Zengshuo Xiao, Ying Zhou, Yun Teng, Xuefu You, Xinyi Yang, Huoqiang Huang and Hao Wang

Molecules 2024, 29(8), 1827; https://doi.org/10.3390/molecules29081827 - 17 Apr 2024

Viewed by 378

Abstract

Antibiotic resistance in Gram-negative bacteria remains one of the most pressing challenges to global public health. Blocking the transportation of lipopolysaccharides (LPS), a crucial component of the outer membrane of Gram-negative bacteria, is considered a promising strategy for drug discovery. In the transportation [...] Read more.

Antibiotic resistance in Gram-negative bacteria remains one of the most pressing challenges to global public health. Blocking the transportation of lipopolysaccharides (LPS), a crucial component of the outer membrane of Gram-negative bacteria, is considered a promising strategy for drug discovery. In the transportation process of LPS, two components of the LPS transport (Lpt) complex, LptA and LptC, are responsible for shuttling LPS across the periplasm to the outer membrane, highlighting their potential as targets for antibacterial drug development. In the current study, a protein–protein interaction (PPI) model of LptA and LptC was constructed, and a molecular screening strategy was employed to search a protein–protein interaction compound library. The screening results indicated that compound 18593 exhibits favorable binding free energy with LptA and LptC. In comparison with the molecular dynamics (MD) simulations on currently known inhibitors, compound 18593 shows more stable target binding ability at the same level. The current study suggests that compound 18593 may exhibit an inhibitory effect on the LPS transport process, making it a promising hit compound for further research. Full article

(This article belongs to the Special Issue Computational Drug Discovery: Methods and Applications)

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13 pages, 8779 KiB

Open AccessArticle

ABC2A: A Straightforward and Fast Method for the Accurate Backmapping of RNA Coarse-Grained Models to All-Atom Structures

by Ya-Zhou Shi, Hao Wu, Sha-Sha Li, Hui-Zhen Li, Ben-Gong Zhang and Ya-Lan Tan

Molecules 2024, 29(6), 1244; https://doi.org/10.3390/molecules29061244 - 11 Mar 2024

Viewed by 471

Abstract

RNAs play crucial roles in various essential biological functions, including catalysis and gene regulation. Despite the widespread use of coarse-grained (CG) models/simulations to study RNA 3D structures and dynamics, their direct application is challenging due to the lack of atomic detail. Therefore, the [...] Read more.

RNAs play crucial roles in various essential biological functions, including catalysis and gene regulation. Despite the widespread use of coarse-grained (CG) models/simulations to study RNA 3D structures and dynamics, their direct application is challenging due to the lack of atomic detail. Therefore, the reconstruction of full atomic structures is desirable. In this study, we introduced a straightforward method called ABC2A for reconstructing all-atom structures from RNA CG models. ABC2A utilizes diverse nucleotide fragments from known structures to assemble full atomic structures based on the CG atoms. The diversification of assembly fragments beyond standard A-form ones, commonly used in other programs, combined with a highly simplified structure refinement process, ensures that ABC2A achieves both high accuracy and rapid speed. Tests on a recent large dataset of 361 RNA experimental structures (30–692 nt) indicate that ABC2A can reconstruct full atomic structures from three-bead CG models with a mean RMSD of ~0.34 Å from experimental structures and an average runtime of ~0.5 s (maximum runtime < 2.5 s). Compared to the state-of-the-art Arena, ABC2A achieves a ~25% improvement in accuracy and is five times faster in speed. Full article

(This article belongs to the Special Issue Computational Drug Discovery: Methods and Applications)

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19 pages, 2349 KiB

Open AccessArticle

Selectivity Studies and Free Energy Calculations of AKT Inhibitors

by Haizhen A. Zhong and David T. Goodwin

Molecules 2024, 29(6), 1233; https://doi.org/10.3390/molecules29061233 - 10 Mar 2024

Cited by 1 | Viewed by 645

Abstract

Protein kinase B (PKB) or AKT protein is an important target for cancer treatment. Significant advances have been made in developing ATP-competitive inhibitors and allosteric binders targeting AKT1. However, adverse effects or toxicities have been found, and the cutaneous toxicity was found to [...] Read more.

Protein kinase B (PKB) or AKT protein is an important target for cancer treatment. Significant advances have been made in developing ATP-competitive inhibitors and allosteric binders targeting AKT1. However, adverse effects or toxicities have been found, and the cutaneous toxicity was found to be linked to the inhibition of AKT2. Thus, selective inhibition of AKT inhibitors is of significance. Our work, using the Schrödinger Covalent Dock (CovDock) program and the Movable Type (MT)-based free energy calculation (ΔG), yielded small mean errors for the experimentally derived binding free energy (ΔG). The docking data suggested that AKT1 binding may require residues Asn54, Trp80, Tyr272, Asp274, and Asp292, whereas AKT2 binding would expect residues Phe163 and Glu279, and AKT3 binding would favor residues Glu17, Trp79, Phe306, and Glu295. These findings may help guide AKT1-selective or AKT3-selective molecular design while sparing the inhibition of AKT2 to minimize the cutaneous toxicity. Full article

(This article belongs to the Special Issue Computational Drug Discovery: Methods and Applications)

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12 pages, 2083 KiB

Open AccessArticle

Discovery of Novel Metalloenzyme Inhibitors Based on Property Characterization: Strategy and Application for HDAC1 Inhibitors

by Lu Zhang, Yajun Yang, Ying Yang and Zhiyan Xiao

Molecules 2024, 29(5), 1096; https://doi.org/10.3390/molecules29051096 - 29 Feb 2024

Viewed by 637

Abstract

Metalloenzymes are ubiquitously present in the human body and are relevant to a variety of diseases. However, the development of metalloenzyme inhibitors is limited by low specificity and poor drug-likeness associated with metal-binding fragments (MBFs). A generalized drug discovery strategy was established, which [...] Read more.

Metalloenzymes are ubiquitously present in the human body and are relevant to a variety of diseases. However, the development of metalloenzyme inhibitors is limited by low specificity and poor drug-likeness associated with metal-binding fragments (MBFs). A generalized drug discovery strategy was established, which is characterized by the property characterization of zinc-dependent metalloenzyme inhibitors (ZnMIs). Fifteen potential Zn²⁺-binding fragments (ZnBFs) were identified, and a customized pharmacophore feature was defined based on these ZnBFs. The customized feature was set as a required feature and applied to a search for novel inhibitors for histone deacetylase 1 (HDAC1). Ten potential HDAC1 inhibitors were recognized, and one of them (compound 9) was a known potent HDAC1 inhibitor. The results demonstrated the effectiveness of our strategy to identify novel inhibitors for zinc-dependent metalloenzymes. Full article

(This article belongs to the Special Issue Computational Drug Discovery: Methods and Applications)

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12 pages, 1497 KiB

Open AccessArticle

Machine Learning Methods as a Cost-Effective Alternative to Physics-Based Binding Free Energy Calculations

by Nupur Bansal, Ye Wang and Simone Sciabola

Molecules 2024, 29(4), 830; https://doi.org/10.3390/molecules29040830 - 13 Feb 2024

Viewed by 1354

Abstract

The rank ordering of ligands remains one of the most attractive challenges in drug discovery. While physics-based in silico binding affinity methods dominate the field, they still have problems, which largely revolve around forcefield accuracy and sampling. Recent advances in machine learning have [...] Read more.

The rank ordering of ligands remains one of the most attractive challenges in drug discovery. While physics-based in silico binding affinity methods dominate the field, they still have problems, which largely revolve around forcefield accuracy and sampling. Recent advances in machine learning have gained traction for protein–ligand binding affinity predictions in early drug discovery programs. In this article, we perform retrospective binding free energy evaluations for 172 compounds from our internal collection spread over four different protein targets and five congeneric ligand series. We compared multiple state-of-the-art free energy methods ranging from physics-based methods with different levels of complexity and conformational sampling to state-of-the-art machine-learning-based methods that were available to us. Overall, we found that physics-based methods behaved particularly well when the ligand perturbations were made in the solvation region, and they did not perform as well when accounting for large conformational changes in protein active sites. On the other end, machine-learning-based methods offer a good cost-effective alternative for binding free energy calculations, but the accuracy of their predictions is highly dependent on the experimental data available for training the model. Full article

(This article belongs to the Special Issue Computational Drug Discovery: Methods and Applications)

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15 pages, 5427 KiB

Open AccessArticle

Sampling and Mapping Chemical Space with Extended Similarity Indices

by Kenneth López-Pérez, Edgar López-López, José L. Medina-Franco and Ramón Alain Miranda-Quintana

Molecules 2023, 28(17), 6333; https://doi.org/10.3390/molecules28176333 - 30 Aug 2023

Viewed by 1697

Abstract

Visualization of the chemical space is useful in many aspects of chemistry, including compound library design, diversity analysis, and exploring structure–property relationships, to name a few. Examples of notable research areas where the visualization of chemical space has strong applications are drug discovery [...] Read more.

Visualization of the chemical space is useful in many aspects of chemistry, including compound library design, diversity analysis, and exploring structure–property relationships, to name a few. Examples of notable research areas where the visualization of chemical space has strong applications are drug discovery and natural product research. However, the sheer volume of even comparatively small sub-sections of chemical space implies that we need to use approximations at the time of navigating through chemical space. ChemMaps is a visualization methodology that approximates the distribution of compounds in large datasets based on the selection of satellite compounds that yield a similar mapping of the whole dataset when principal component analysis on a similarity matrix is performed. Here, we show how the recently proposed extended similarity indices can help find regions that are relevant to sample satellites and reduce the amount of high-dimensional data needed to describe a library’s chemical space. Full article

(This article belongs to the Special Issue Computational Drug Discovery: Methods and Applications)

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14 pages, 8482 KiB

Open AccessArticle

cMolGPT: A Conditional Generative Pre-Trained Transformer for Target-Specific De Novo Molecular Generation

by Ye Wang, Honggang Zhao, Simone Sciabola and Wenlu Wang

Molecules 2023, 28(11), 4430; https://doi.org/10.3390/molecules28114430 - 30 May 2023

Cited by 10 | Viewed by 3815

Abstract

Deep generative models applied to the generation of novel compounds in small-molecule drug design have attracted a lot of attention in recent years. To design compounds that interact with specific target proteins, we propose a Generative Pre-Trained Transformer (GPT)-inspired model for de novo [...] Read more.

Deep generative models applied to the generation of novel compounds in small-molecule drug design have attracted a lot of attention in recent years. To design compounds that interact with specific target proteins, we propose a Generative Pre-Trained Transformer (GPT)-inspired model for de novo target-specific molecular design. By implementing different keys and values for the multi-head attention conditional on a specified target, the proposed method can generate drug-like compounds both with and without a specific target. The results show that our approach (cMolGPT) is capable of generating SMILES strings that correspond to both drug-like and active compounds. Moreover, the compounds generated from the conditional model closely match the chemical space of real target-specific molecules and cover a significant portion of novel compounds. Thus, the proposed Conditional Generative Pre-Trained Transformer (cMolGPT) is a valuable tool for de novo molecule design and has the potential to accelerate the molecular optimization cycle time. Full article

(This article belongs to the Special Issue Computational Drug Discovery: Methods and Applications)

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28 pages, 7856 KiB

Open AccessArticle

Identification of Some Glutamic Acid Derivatives with Biological Potential by Computational Methods

by Octavia-Laura Moldovan, Alexandra Sandulea, Ioana-Andreea Lungu, Șerban Andrei Gâz and Aura Rusu

Molecules 2023, 28(10), 4123; https://doi.org/10.3390/molecules28104123 - 16 May 2023

Viewed by 2561

Abstract

Glutamic acid is a non-essential amino acid involved in multiple metabolic pathways. Of high importance is its relationship with glutamine, an essential fuel for cancer cell development. Compounds that can modify glutamine or glutamic acid behaviour in cancer cells have resulted in attractive [...] Read more.

Glutamic acid is a non-essential amino acid involved in multiple metabolic pathways. Of high importance is its relationship with glutamine, an essential fuel for cancer cell development. Compounds that can modify glutamine or glutamic acid behaviour in cancer cells have resulted in attractive anticancer therapeutic alternatives. Based on this idea, we theoretically formulated 123 glutamic acid derivatives using Biovia Draw. Suitable candidates for our research were selected among them. For this, online platforms and programs were used to describe specific properties and their behaviour in the human organism. Nine compounds proved to have suitable or easy to optimise properties. The selected compounds showed cytotoxicity against breast adenocarcinoma, lung cancer cell lines, colon carcinoma, and T cells from acute leukaemia. Compound 2Ba5 exhibited the lowest toxicity, and derivative 4Db6 exhibited the most intense bioactivity. Molecular docking studies were also performed. The binding site of the 4Db6 compound in the glutamine synthetase structure was determined, with the D subunit and cluster 1 being the most promising. In conclusion, glutamic acid is an amino acid that can be manipulated very easily. Therefore, molecules derived from its structure have great potential to become innovative drugs, and further research on these will be conducted. Full article

(This article belongs to the Special Issue Computational Drug Discovery: Methods and Applications)

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24 pages, 7385 KiB

Open AccessArticle

Pharmacophore-Based Virtual Screening and In-Silico Explorations of Biomolecules (Curcumin Derivatives) of Curcuma longa as Potential Lead Inhibitors of ERBB and VEGFR-2 for the Treatment of Colorectal Cancer

by Syeda Abida Ejaz, Mubashir Aziz, Mohamed Fawzy Ramadan, Ammara Fayyaz and Muhammad Sajjad Bilal

Molecules 2023, 28(10), 4044; https://doi.org/10.3390/molecules28104044 - 12 May 2023

Cited by 2 | Viewed by 2024

Abstract

The newly FDA-approved drug, Axitinib, is an effective therapy against RTKs, but it possesses severe adverse effects like hypertension, stomatitis, and dose-dependent toxicity. In order to ameliorate Axitinib’s downsides, the current study is expedited to search for energetically stable and optimized pharmacophore features [...] Read more.

The newly FDA-approved drug, Axitinib, is an effective therapy against RTKs, but it possesses severe adverse effects like hypertension, stomatitis, and dose-dependent toxicity. In order to ameliorate Axitinib’s downsides, the current study is expedited to search for energetically stable and optimized pharmacophore features of 14 curcumin (1,7-bis(4-hydroxy-3-methoxyphenyl)hepta-1,6-diene-3,5-dione) derivatives. The rationale behind the selection of curcumin derivatives is their reported anti-angiogenic and anti-cancer properties. Furthermore, they possessed a low molecular weight and a low toxicity profile. In the current investigation, the pharmacophore model-based drug design, facilitates the filtering of curcumin derivatives as VEGFR2 interfacial inhibitors. Initially, the Axitinib scaffold was used to build a pharmacophore query model against which curcumin derivatives were screened. Then, top hits from pharmacophore virtual screening were subjected to in-depth computational studies such as molecular docking, density functional theory (DFT) studies, molecular dynamics (MD) simulations, and ADMET property prediction. The findings of the current investigation revealed the substantial chemical reactivity of the compounds. Specifically, compounds S8, S11, and S14 produced potential molecular interactions against all four selected protein kinases. Docking scores of −41.48 and −29.88 kJ/mol for compounds S8 against VEGFR1 and VEGFR3, respectively, were excellent. Whereas compounds S11 and S14 demonstrated the highest inhibitory potential against ERBB and VEGFR2, with docking scores of −37.92 and −38.5 kJ/mol against ERBB and −41.2 and −46.5 kJ/mol against VEGFR-2, respectively. The results of the molecular docking studies were further correlated with the molecular dynamics simulation studies. Moreover, HYDE energy was calculated through SeeSAR analysis, and the safety profile of the compounds was predicted through ADME studies. Full article

(This article belongs to the Special Issue Computational Drug Discovery: Methods and Applications)

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21 pages, 5678 KiB

Open AccessArticle

Virtual Screening for Identification of Dual Inhibitors against CDK4/6 and Aromatase Enzyme

by Tenzin Adon, Dhivya Shanmugarajan, Hissana Ather, Shaik Mohammad Asif Ansari, Umme Hani, SubbaRao V. Madhunapantula and Yogish Kumar Honnavalli

Molecules 2023, 28(6), 2490; https://doi.org/10.3390/molecules28062490 - 08 Mar 2023

Viewed by 2379

Abstract

CDK4/6 and aromatase are prominent targets for breast cancer drug discovery and are involved in abnormal cell proliferation and growth. Although aromatase inhibitors have proven to be effective (for example exemestane, anastrozole, letrozole), resistance to treatment eventually occurs through the activation of alternative [...] Read more.

CDK4/6 and aromatase are prominent targets for breast cancer drug discovery and are involved in abnormal cell proliferation and growth. Although aromatase inhibitors have proven to be effective (for example exemestane, anastrozole, letrozole), resistance to treatment eventually occurs through the activation of alternative signaling pathways, thus evading the antiproliferative effects of aromatase inhibitors. One of the evasion pathways is Cylin D-CDK4/6-Rb signaling that promotes tumor proliferation and resistance to aromatase inhibitors. There is significant evidence that the sequential inhibition of both proteins provides therapeutic benefits over the inhibition of one target. The basis of this study objective is the identification of molecules that are likely to inhibit both CDK4/6 and aromatase by computational chemistry techniques, which need further biochemical studies to confirm. Initially, a structure-based pharmacophore model was constructed for each target to screen the sc-PDB database. Consequently, pharmacophore screening and molecular docking were performed to evaluate the potential lead candidates that effectively mapped both of the target pharmacophore models. Considering abemaciclib (CDK4/6 inhibitor) and exemestane (aromatase inhibitor) as reference drugs, four potential virtual hit candidates (1, 2, 3, and 4) were selected based on their fit values and binding interaction after screening a sc-PDB database. Further, molecular dynamics simulation studies solidify the stability of the lead candidate complexes. In addition, ADMET and DFT calculations bolster the lead candidates. Hence, these combined computational approaches will provide a better therapeutic potential for developing CDK4/6-aromatase dual inhibitors for HR+ breast cancer therapy. Full article

(This article belongs to the Special Issue Computational Drug Discovery: Methods and Applications)

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21 pages, 7591 KiB

Open AccessReview

Experimental and Computational Methods to Assess Central Nervous System Penetration of Small Molecules

by Mayuri Gupta, Jun Feng and Govinda Bhisetti

Molecules 2024, 29(6), 1264; https://doi.org/10.3390/molecules29061264 - 13 Mar 2024

Viewed by 1280

Abstract

In CNS drug discovery, the estimation of brain exposure to lead compounds is critical for their optimization. Compounds need to cross the blood–brain barrier (BBB) to reach the pharmacological targets in the CNS. The BBB is a complex system involving passive and active [...] Read more.

In CNS drug discovery, the estimation of brain exposure to lead compounds is critical for their optimization. Compounds need to cross the blood–brain barrier (BBB) to reach the pharmacological targets in the CNS. The BBB is a complex system involving passive and active mechanisms of transport and efflux transporters such as P-glycoproteins (P-gp) and breast cancer resistance protein (BCRP), which play an essential role in CNS penetration of small molecules. Several in vivo, in vitro, and in silico methods are available to estimate human brain penetration. Preclinical species are used as in vivo models to understand unbound brain exposure by deriving the Kp,uu parameter and the brain/plasma ratio of exposure corrected with the plasma and brain free fraction. The MDCK-mdr1 (Madin Darby canine kidney cells transfected with the MDR1 gene encoding for the human P-gp) assay is the commonly used in vitro assay to estimate compound permeability and human efflux. The in silico methods to predict brain exposure, such as CNS MPO, CNS BBB scores, and various machine learning models, help save costs and speed up compound discovery and optimization at all stages. These methods enable the screening of virtual compounds, building of a CNS penetrable compounds library, and optimization of lead molecules for CNS penetration. Therefore, it is crucial to understand the reliability and ability of these methods to predict CNS penetration. We review the in silico, in vitro, and in vivo data and their correlation with each other, as well as assess published experimental and computational approaches to predict the BBB penetrability of compounds. Full article

(This article belongs to the Special Issue Computational Drug Discovery: Methods and Applications)

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